A novel method for feature extraction and recognition called Kernel Fuzzy Discriminant Analysis (KFDA) is proposed in this paper to deal with recognition problems, e.g., for images. The KFDA method is obtained by combining the advantages of fuzzy methods and a kernel trick. Based on the orthogonal-triangular decomposition of a matrix and Singular Value Decomposition (SVD), two different variant...

Advances in digital image processing were increased in the past few years. Blind source separation is one of the important research area with numerous applications in signal processing, image processing, telecommunication and speech recognition. In this paper the Blind Source Separation is performed using Slow Feature Analysis(SFA). It is necessary to use multivariate SFA instead of univariate ...

Convolutional neural networks have become a main tool for solving many machine vision and machine learning problems. A major element of these networks is the convolution operator which essentially computes the inner product between a weight vector and the vectorized image patches extracted by sliding a window in the image planes of the previous layer. In this paper, we propose two classes of su...

This paper proposes a method for recognizing handshapes by using multi-viewpoint image sets. The recognition of a handshape is a difficult problem, as appearance of the hand changes largely depending on viewpoint, illumination conditions and individual characteristics. To overcome this problem, we apply the Kernel Orthogonal Mutual Subspace Method (KOMSM) to shift-invariance features obtained f...

We describe a data complexity approach to kernel selection based on the behavior of polynomial and Gaussian kernels. Our results show how the use of a Gaussian kernel produces a gram matrix with useful local information that has no equivalent counterpart in polynomial kernels. By exploiting neighborhood information embedded by data complexity measures, we are able to carry out a form of meta-ge...